
# Import des librairies
import numpy as np # linear algebra
import pandas as pd # data processing
import matplotlib.pyplot as plt
import seaborn as sns
import dataprep.eda as eda
import pingouin as pg
import missingno as msno
bleu=(70/255, 130/255, 180/255)

pd.set_option('display.max_row', 60)
pd.set_option('display.max_column', 60)

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\outdated\utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.0, the latest is 0.2.1.
Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.
  return warn(
C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\outdated\utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.3.10, the latest is 0.3.11.
Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.
  return warn(


Buildings2015=pd.read_csv('2015-building-energy-benchmarking.csv')
meta_2015 = pd.read_json('socrata_metadata_2015-building-energy-benchmarking.json',
                             orient='index')


# Affichage du chargement
display(Buildings2015.shape,Buildings2015.head(2))
meta_2015

(3340, 47)


# Création d'un dataframe contenant les métadonnées
meta_data_2015=pd.DataFrame(meta_2015.T['columns'][0])
meta_data_2015


# Noms des variables année 2015
with pd.option_context('display.max_colwidth', None):
    display(meta_data_2015[['name','description']])


# Suppression des doublons
Buildings2015.drop_duplicates(inplace=True)
Buildings2015.shape

(3340, 47)


Buildings2016=pd.read_csv('2016-building-energy-benchmarking.csv')
meta_2016 = pd.read_json('socrata_metadata_2016-building-energy-benchmarking.json',
                             orient='index')


# Affichage du chargement
display(Buildings2016.shape,Buildings2016.head(2))
meta_2016

(3376, 46)


# Création d'un dataframe contenant les métadonnées
meta_data_2016=pd.DataFrame(meta_2016.T['columns'][0])
meta_data_2016


# Noms des variables année 2016
with pd.option_context('display.max_colwidth', None):
    display(meta_data_2016[['name','description']])


Buildings2016.drop_duplicates(inplace=True)
Buildings2016.shape

(3376, 46)


col_2015=meta_data_2015['name'].tolist()
col_2016=meta_data_2016['name'].tolist()

col_2015_notin_2016 = [name for name in col_2015 if name not in col_2016]
print(' Colonnes 2015 absentes de 2016 :\n',col_2015_notin_2016,'\n')
col_2016_notin_2015 = [name for name in col_2016 if name not in col_2015]
print(' Colonnes 2016 absentes de 2015 :\n',col_2016_notin_2015)

 Colonnes 2015 absentes de 2016 :
 ['Location', 'OtherFuelUse(kBtu)', 'GHGEmissions(MetricTonsCO2e)', 'GHGEmissionsIntensity(kgCO2e/ft2)', 'Comment', '2010 Census Tracts', 'Seattle Police Department Micro Community Policing Plan Areas', 'City Council Districts', 'SPD Beats', 'Zip Codes'] 

 Colonnes 2016 absentes de 2015 :
 ['Address', 'City', 'State', 'ZipCode', 'Latitude', 'Longitude', 'Comments', 'TotalGHGEmissions', 'GHGEmissionsIntensity']


Buildings2015.rename(columns={'GHGEmissions(MetricTonsCO2e)': 'TotalGHGEmissions',
                              'GHGEmissionsIntensity(kgCO2e/ft2)': 'GHGEmissionsIntensity'
                             }, inplace=True)


col_2015=Buildings2015.columns.to_list()
col_2016=Buildings2016.columns.to_list()

col_2015_notin_2016 = [name for name in col_2015 if name not in col_2016]
print('Colonnes 2015 absentes de 2016 :\n',col_2015_notin_2016,'\n')
col_2016_notin_2015 = [name for name in col_2016 if name not in col_2015]
print(' Colonnes 2016 absentes de 2015 :\n',col_2016_notin_2015)

Colonnes 2015 absentes de 2016 :
 ['Location', 'OtherFuelUse(kBtu)', 'Comment', '2010 Census Tracts', 'Seattle Police Department Micro Community Policing Plan Areas', 'City Council Districts', 'SPD Beats', 'Zip Codes'] 

 Colonnes 2016 absentes de 2015 :
 ['Address', 'City', 'State', 'ZipCode', 'Latitude', 'Longitude', 'Comments']


Buildings2015.drop(col_2015_notin_2016, axis=1, inplace=True)
Buildings2016.drop(col_2016_notin_2015, axis=1, inplace=True)

#Suppression de TaxParcelIdentificationNumber pour le mêmes raisons
Buildings2015.drop('TaxParcelIdentificationNumber', axis=1, inplace=True)
Buildings2016.drop('TaxParcelIdentificationNumber', axis=1, inplace=True)


data=pd.concat([Buildings2015,Buildings2016],ignore_index=True)
data.shape

(6716, 38)


msno.matrix(data,color=bleu)

<AxesSubplot:>


# Formatagge des noms des propriétés
data['PropertyName']=data['PropertyName'].apply(lambda s : s.upper())


data['PropertyName'].duplicated(keep=False).sum()

1844


data['OSEBuildingID'].duplicated(keep=False).sum()

6568


# filtrage sur les noms des propriétés
data.drop_duplicates(subset='OSEBuildingID',keep='last',inplace=True)
data.shape

(3432, 38)


# Filtrage sur les variables des batiments restants
data.duplicated(keep=False).sum()

0


data.drop('OSEBuildingID',axis=1,inplace=True)


msno.matrix(data,color=bleu)

<AxesSubplot:>


# Exclusion des sujets pour lesquels on ne connait pas les cibles
data=data.dropna(subset=['SiteEnergyUse(kBtu)','TotalGHGEmissions'],axis=0)
print('Reste',data.shape[0],'batiments pour lesquels on connaît ENERGY USE et TotalGHGEmissions ainsi que',data.shape[1]-1, 'autres indicateurs.')

Reste 3422 batiments pour lesquels on connaît ENERGY USE et TotalGHGEmissions ainsi que 36 autres indicateurs.


msno.matrix(data,color=bleu)

<AxesSubplot:>


exclues=['SiteEUI(kBtu/sf)',
       'SiteEUIWN(kBtu/sf)', 'SourceEUI(kBtu/sf)', 'SourceEUIWN(kBtu/sf)',
       'SiteEnergyUseWN(kBtu)', 'SteamUse(kBtu)', 'Electricity(kWh)',
       'Electricity(kBtu)', 'NaturalGas(therms)', 'NaturalGas(kBtu)',
       'GHGEmissionsIntensity']
data.drop(exclues,axis=1,inplace=True)


msno.matrix(data,color=bleu)
print('Reste',data.shape[0],'batiments pour lesquels on connaît ENERGY USE et TotalGHGEmissions ainsi que',data.shape[1]-1, 'autres indicateurs.')

Reste 3422 batiments pour lesquels on connaît ENERGY USE et TotalGHGEmissions ainsi que 25 autres indicateurs.


data.describe(include='all')


data[data['NumberofFloors'] <=0]


# Correction du nombre d'étages : au moins 1 étage
data['NumberofFloors']=data['NumberofFloors'].apply(lambda x: x if x>0 else 1)


# Correction du nombre de bâtiments: au moins 1
data['NumberofBuildings']=data['NumberofBuildings'].apply(lambda x: x if x>0 else 1)


data.drop(data[data['SiteEnergyUse(kBtu)'] <= 0].index,inplace=True)
data.shape

(3404, 26)


data.drop(data[data['TotalGHGEmissions'] <= 0].index,inplace=True)
data.shape

(3401, 26)


# Function calculant le nombre de valeurs manquantes (Nan) par colonnes 
def missing_values(table,tri=True,sens=True):
        # Total Nan
        mis_val = table.isnull().sum()
        
        # Pourcentage de  Nan
        mis_val_percent = table.isnull().sum() / len(table) * 100
        
        # Data Frame contenant les résultats
        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
        
        # Nommage des colonnes
        mis_val_table_ren_columns = mis_val_table.rename(
        columns = {0 : 'Valeurs manquantes', 1 : '% du Total des valeurs'})
        
        if tri:
            # Avec tri sur le pourcentage de valeurs manquantesSort the table by percentage of missing descending
            mis_val_table_ren_columns = mis_val_table_ren_columns[
            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
            '% du Total des valeurs', ascending=sens).round(2)
        else:
            # Sans tri sur le poucentage de valeurs manquantes
            mis_val_table_ren_columns = mis_val_table_ren_columns[
            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
            '% du Total des valeurs', ascending=False).round(2)
        
        # Affichage du nom de la table analysée
        # du nombre de colonnes concernées par des valeurs manquantes
        display (" Data Frame a " + str(table.shape[1]) + " colonnes.\n"      
            "Dont " + str(mis_val_table_ren_columns.shape[0]) +
              " colonnes contiennent des valeurs manquantes.")
        
        # Retourne la tables contenant les stats par colonnes
        return mis_val_table_ren_columns


missing_values(data,True,False)

' Data Frame a 26 colonnes.\nDont 10 colonnes contiennent des valeurs manquantes.'


data['Outlier']=data['Outlier'].str.lower()
data['Outlier'].value_counts()

low outlier     26
high outlier     9
Name: Outlier, dtype: int64


mask_high=data['Outlier']== 'high outlier'
data[mask_high]


mask_low=data['Outlier']== 'low outlier'
data[mask_low]


data['Outlier'].fillna('normal',inplace=True)


missing_values(data,True)

' Data Frame a 26 colonnes.\nDont 9 colonnes contiennent des valeurs manquantes.'


data.dropna(subset=['ListOfAllPropertyUseTypes'],axis=0,inplace=True)
data.shape

(3385, 26)


temp=data[data['LargestPropertyUseType'].isna()]
display(temp)
temp.shape

(11, 26)


data.dropna(subset=['LargestPropertyUseType'],axis=0,inplace=True)
data.shape

(3374, 26)


modalités=pd.DataFrame(data['ListOfAllPropertyUseTypes'].value_counts())
display(modalités)


display('Avant: ',data.shape)
data['ListOfAllPropertyUseTypes']=data['ListOfAllPropertyUseTypes'].apply(lambda s :s.replace(s[ s.find( '(' ) : s.find( ')' )+1 ],s[ s.find( '(' ) : s.find( ')' )+1 ].replace(', ','/')))
test=data['ListOfAllPropertyUseTypes'].str.split(pat=', ',expand=True)
# test=data.pop('ListOfAllPropertyUseTypes').str.split(pat=', ',expand=True)
test.drop(0,axis=1,inplace=True)
test.fillna('None',axis=1,inplace=True)
test.columns=['Use'+str(s) for s in test.columns]
data=pd.concat([data,test],axis=1)
display('Aprés :',data.shape)

'Avant: '

(3374, 26)

'Aprés :'

(3374, 34)


data.drop('ListOfAllPropertyUseTypes',axis=1,inplace=True)


data[data['SecondLargestPropertyUseTypeGFA'].isna()]['SecondLargestPropertyUseType'].unique()

array([nan], dtype=object)


data[data['SecondLargestPropertyUseTypeGFA'].isna()]['Use2'].unique()

array(['None', 'Parking'], dtype=object)


data[data['ThirdLargestPropertyUseType'].isna()]['ThirdLargestPropertyUseType'].unique()

array([nan], dtype=object)


data[data['ThirdLargestPropertyUseType'].isna()]['Use3'].unique()

array(['None'], dtype=object)


data['SecondLargestPropertyUseTypeGFA'].fillna(0,inplace=True)
data['SecondLargestPropertyUseType'].fillna('None',inplace=True)

data['ThirdLargestPropertyUseTypeGFA'].fillna(0,inplace=True)
data['ThirdLargestPropertyUseType'].fillna('None',inplace=True)


data.drop(['Use1','Use2','Use3'],axis=1,inplace=True)


missing_values(data)

' Data Frame a 30 colonnes.\nDont 2 colonnes contiennent des valeurs manquantes.'


# YearsENERGYSTARCertified a trop de valeurs manquantes pour pouvoir être exploitée
data.drop('YearsENERGYSTARCertified',axis=1,inplace=True)


# #fonction permettant de tracer des boxplots simples avec distribution
# import matplotlib.pyplot as plt

def Distribution(Sx,titre='',unit_text='',unite=1,fliers=True,tab=10):
    # série à traiter, titre de la figure, xlabel, unité abscisses, legende unité , tab= espacement des affichages text
    # fliers True/False
    
    
    f, (ax1, ax2) = plt.subplots(2, 1,figsize=(10,4),sharex=True, gridspec_kw={'height_ratios': [3, 1],'hspace':.3})
    f.suptitle(titre,y=1,fontsize=20)
    # Boxplot 
    sns.boxplot(x=Sx,ax=ax2,orient='h',showfliers = fliers, showmeans=True,meanprops={"marker":"d"})
    xmin, xmax, ymin, ymax = ax2.axis()
    # Formatage des valeurs du bandeau moyenne, ecart type,....
    def form (x): return str('{:.1f}'.format(x)).ljust(tab)
    # Formatage des unités abscisses
    val=ax2.get_xticks()    
    ax2.set_xticklabels(val/unite)
    ax2.tick_params(labelsize=14)
    # Affichage unités abscisses
    ax2.set_xlabel(unit_text,x=1, ha='right',fontsize=14) 
     # Ajout des indicateurs statistiques
    print(type(unite))
    if fliers:
        ax2.set_title('Min= '+form(Sx.min()/unite)+
                  'Médiane= '+form(Sx.median()/unite)+
                  'Moyenne= '+form(Sx.mean()/unite)+
                  '\u03C3= '+form(Sx.std()/unite)+
                  'Max= '+form(Sx.max()/unite),
                  fontsize=14)
    
    # Histogramme
    
    ax1.set_title(' skewness : '+str(Sx.skew())+'              kurtosis : '+str(Sx.kurtosis())) 
    ax1.tick_params(labelsize=14)
    ax1.set_xlim(xmin,xmax)
    sns.histplot(data=Sx,stat='count',bins='auto',discrete=False,kde=True,ax=ax1)   
#     plt.show()
    return


cible1='SiteEnergyUse(kBtu)'
Distribution(data[cible1],'Distribution de '+cible1,'(en millions de kBtu)',unite=1E6)
Distribution(data[cible1],'Distribution de '+cible1+' sans outliers','(en millions de kBtu)',unite=1E6,fliers=False)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>


cible2='TotalGHGEmissions'
Distribution(data[cible2],'Distribution de '+cible2,'(en milliers de tonnes CO2)',unite=1E3)
Distribution(data[cible2],'Distribution de '+cible2+' sans outliers','(en milliers de tonnes CO2)',unite=1E3,fliers=False)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>


temp=data.pop('TotalGHGEmissions')
data=pd.concat([temp,data],axis=1)
temp=data.pop('SiteEnergyUse(kBtu)')
data=pd.concat([temp,data],axis=1)
data.sample(5)


Lst_qual=pd.DataFrame({'Variables quantitatives':data.iloc[:,1:].select_dtypes('object').columns}).T
Lst_qual.T


def qual_plot(table,col,kind='count'):
    g=sns.catplot(data=table,y=col,kind=kind,orient="h", height=10, aspect=1)
    (g.set_axis_labels('Effectifs',col )
      .set_titles('Titre'))
    plt.show()


modalités=data['BuildingType'].value_counts()
modalités

NonResidential          1472
Multifamily LR (1-4)    1016
Multifamily MR (5-9)     583
Multifamily HR (10+)     108
Nonresidential COS        85
SPS-District K-12         84
Campus                    25
Nonresidential WA          1
Name: BuildingType, dtype: int64


#                   Consigne explicite du projet
# votre équipe s’intéresse de près aux émissions des bâtiments non destinés à l’habitation.
#
BuildingType_a_exclure=['Multifamily LR (1-4)',
                        'Multifamily MR (5-9)',
                        'Multifamily HR (10+)',
#                      catégories sous représentées
#                         'Campus',
#                         'Nonresidential COS',
#                         'SPS-District K-12'
                       ]


data.drop(data[data['BuildingType'].isin(BuildingType_a_exclure)].index,axis=0,inplace=True)


data.shape

(1667, 29)


sns.boxplot(data=data,x='SiteEnergyUse(kBtu)',y='BuildingType',showfliers=True)

<AxesSubplot:xlabel='SiteEnergyUse(kBtu)', ylabel='BuildingType'>


display(cible1,pg.welch_anova(data=data,dv=cible1,between='BuildingType'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='BuildingType'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


modalités=data['PrimaryPropertyType'].value_counts()
display(modalités)
modalités.shape

Small- and Mid-Sized Office    296
Other                          254
Warehouse                      187
Large Office                   176
K-12 School                    126
Mixed Use Property             113
Retail Store                    92
Hotel                           75
Worship Facility                71
Distribution Center             53
Medical Office                  42
Supermarket / Grocery Store     39
Self-Storage Facility           27
University                      24
Residence Hall                  21
Senior Care Community           20
Refrigerated Warehouse          12
Restaurant                      11
Laboratory                      10
Hospital                        10
Office                           3
Low-Rise Multifamily             3
Non-Refrigerated Warehouse       1
Restaurant\n                     1
Name: PrimaryPropertyType, dtype: int64

(24,)


data['PrimaryPropertyType']=data['PrimaryPropertyType'].apply(lambda x: x.replace('\n',''))

data['PrimaryPropertyType']=data['PrimaryPropertyType'].apply(lambda x: x.replace(' / ','/'))

data['PrimaryPropertyType']=data['PrimaryPropertyType'].apply(lambda x: x.replace('/Dormitory',''))

data['PrimaryPropertyType']=data['PrimaryPropertyType'].apply(lambda x: x.replace('Non-Refrigerated Warehouse','Warehouse'))

# data['PrimaryPropertyType']=data['PrimaryPropertyType'].apply(lambda x: x.replace('College/',''))

data.loc[data[data['PrimaryPropertyType']=='Office'].index,'PrimaryPropertyType']='Small- and Mid-Sized Office'

data.drop(data[data['PrimaryPropertyType'].str.contains('Multifamily')].index,axis=0,inplace=True)

modalités=data['PrimaryPropertyType'].value_counts()
display(modalités)
modalités.shape

Small- and Mid-Sized Office    299
Other                          254
Warehouse                      188
Large Office                   176
K-12 School                    126
Mixed Use Property             113
Retail Store                    92
Hotel                           75
Worship Facility                71
Distribution Center             53
Medical Office                  42
Supermarket/Grocery Store       39
Self-Storage Facility           27
University                      24
Residence Hall                  21
Senior Care Community           20
Refrigerated Warehouse          12
Restaurant                      12
Hospital                        10
Laboratory                      10
Name: PrimaryPropertyType, dtype: int64

(20,)


sns.boxplot(data=data,x='SiteEnergyUse(kBtu)',y='PrimaryPropertyType',showfliers=False)

<AxesSubplot:xlabel='SiteEnergyUse(kBtu)', ylabel='PrimaryPropertyType'>


display(cible1,pg.welch_anova(data=data,dv=cible1,between='PrimaryPropertyType'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='PrimaryPropertyType'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


modalités=pd.DataFrame(data['PropertyName'].value_counts())
display(modalités)
modalités.shape

(1658, 1)


modalités=pd.DataFrame(data['Neighborhood'].value_counts())
display(modalités)
modalités.shape

(19, 1)


data['Neighborhood']=data['Neighborhood'].apply(lambda s : s[:8] if s=='DELRIDGE NEIGHBORHOODS' else s)


data['Neighborhood']=data['Neighborhood'].apply(lambda s : s.capitalize())


modalités=pd.DataFrame(data['Neighborhood'].value_counts())
display(modalités)
modalités.shape

(13, 1)


sns.boxplot(data=data,x='SiteEnergyUse(kBtu)',y='Neighborhood',showfliers=False)

<AxesSubplot:xlabel='SiteEnergyUse(kBtu)', ylabel='Neighborhood'>


display(cible1,pg.welch_anova(data=data,dv=cible1,between='Neighborhood'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='Neighborhood'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


modalités=data['LargestPropertyUseType'].value_counts()
display(modalités)
modalités.shape

Office                                                  506
Non-Refrigerated Warehouse                              200
K-12 School                                             126
Other                                                   101
Retail Store                                            100
Hotel                                                    76
Worship Facility                                         71
Distribution Center                                      54
Medical Office                                           44
Supermarket/Grocery Store                                40
Other - Recreation                                       31
Parking                                                  30
Self-Storage Facility                                    27
College/University                                       24
Residence Hall/Dormitory                                 22
Other - Entertainment/Public Assembly                    21
Senior Care Community                                    20
Laboratory                                               13
Restaurant                                               13
Refrigerated Warehouse                                   12
Social/Meeting Hall                                      10
Hospital (General Medical & Surgical)                    10
Multifamily Housing                                       9
Manufacturing/Industrial Plant                            8
Automobile Dealership                                     6
Repair Services (Vehicle, Shoe, Locksmith, etc)           6
Strip Mall                                                6
Other - Services                                          5
Museum                                                    5
Other - Lodging/Residential                               5
Fitness Center/Health Club/Gym                            5
Performing Arts                                           4
Library                                                   4
Urgent Care/Clinic/Other Outpatient                       4
Other - Mall                                              4
Financial Office                                          4
Bank Branch                                               4
Other/Specialty Hospital                                  4
Data Center                                               3
Other - Education                                         3
Prison/Incarceration                                      3
Other - Public Services                                   2
Lifestyle Center                                          2
Adult Education                                           2
Pre-school/Daycare                                        2
Other - Restaurant/Bar                                    2
Other - Utility                                           2
Personal Services (Health/Beauty, Dry Cleaning, etc)      1
Wholesale Club/Supercenter                                1
Fire Station                                              1
Movie Theater                                             1
Police Station                                            1
Outpatient Rehabilitation/Physical Therapy                1
Courthouse                                                1
Residential Care Facility                                 1
Food Service                                              1
Name: LargestPropertyUseType, dtype: int64

(56,)


display(cible1,pg.welch_anova(data=data,dv=cible1,between='LargestPropertyUseType'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='LargestPropertyUseType'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


qual_plot(data,'LargestPropertyUseType')


modalités=data['SecondLargestPropertyUseType'].value_counts()
display(modalités)
modalités.shape

None                                                    802
Parking                                                 342
Office                                                  169
Retail Store                                             84
Other                                                    45
Non-Refrigerated Warehouse                               30
Restaurant                                               29
Fitness Center/Health Club/Gym                           16
Data Center                                              13
Multifamily Housing                                      12
Other - Services                                         11
Supermarket/Grocery Store                                10
Medical Office                                            9
Laboratory                                                8
Bank Branch                                               7
Other - Entertainment/Public Assembly                     7
Distribution Center                                       7
K-12 School                                               5
Swimming Pool                                             4
Repair Services (Vehicle, Shoe, Locksmith, etc)           4
Other - Recreation                                        4
Movie Theater                                             3
Hotel                                                     3
Worship Facility                                          3
Self-Storage Facility                                     3
Other - Public Services                                   3
Automobile Dealership                                     2
Adult Education                                           2
Other - Restaurant/Bar                                    2
Refrigerated Warehouse                                    2
Food Sales                                                2
Other - Education                                         2
Bar/Nightclub                                             2
Performing Arts                                           2
Manufacturing/Industrial Plant                            2
Financial Office                                          1
Food Service                                              1
Pre-school/Daycare                                        1
Social/Meeting Hall                                       1
Convenience Store without Gas Station                     1
Hospital (General Medical & Surgical)                     1
College/University                                        1
Personal Services (Health/Beauty, Dry Cleaning, etc)      1
Vocational School                                         1
Courthouse                                                1
Other - Lodging/Residential                               1
Residence Hall/Dormitory                                  1
Enclosed Mall                                             1
Name: SecondLargestPropertyUseType, dtype: int64

(48,)


display(cible1,pg.welch_anova(data=data,dv=cible1,between='SecondLargestPropertyUseType'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='SecondLargestPropertyUseType'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


qual_plot(data,'SecondLargestPropertyUseType')


modalités=data['ThirdLargestPropertyUseType'].value_counts()
modalités

None                                                    1310
Office                                                    49
Retail Store                                              48
Parking                                                   46
Other                                                     41
Restaurant                                                31
Non-Refrigerated Warehouse                                15
Data Center                                               14
Multifamily Housing                                       12
Swimming Pool                                             10
Medical Office                                             9
Pre-school/Daycare                                         8
Food Service                                               8
Social/Meeting Hall                                        8
Other - Restaurant/Bar                                     5
Bank Branch                                                5
Financial Office                                           4
Other - Entertainment/Public Assembly                      4
Fitness Center/Health Club/Gym                             4
Distribution Center                                        3
Refrigerated Warehouse                                     2
Convenience Store without Gas Station                      2
Personal Services (Health/Beauty, Dry Cleaning, etc)       2
Self-Storage Facility                                      2
Other - Technology/Science                                 2
Other - Recreation                                         2
Other - Services                                           2
Laboratory                                                 2
Other - Education                                          2
K-12 School                                                2
Other - Utility                                            1
Strip Mall                                                 1
Fast Food Restaurant                                       1
Supermarket/Grocery Store                                  1
Other/Specialty Hospital                                   1
Manufacturing/Industrial Plant                             1
Worship Facility                                           1
Bar/Nightclub                                              1
Vocational School                                          1
Hotel                                                      1
Name: ThirdLargestPropertyUseType, dtype: int64


display(cible1,pg.welch_anova(data=data,dv=cible1,between='ThirdLargestPropertyUseType'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='ThirdLargestPropertyUseType'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


qual_plot(data,'ThirdLargestPropertyUseType')


pd.DataFrame(data['DefaultData'].value_counts())


pd.DataFrame(data['DefaultData'].value_counts()).index

Index([False, True, 'No', 'Yes'], dtype='object')


data['DefaultData']=data['DefaultData'].apply(lambda s : 'No' if s==False else s)
data['DefaultData']=data['DefaultData'].apply(lambda s : 'Yes' if s==True else s)
pd.DataFrame(data['DefaultData'].value_counts())


display(cible1,pg.welch_anova(data=data,dv=cible1,between='DefaultData'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='DefaultData'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


data.drop('DefaultData',axis=1,inplace=True)


pd.DataFrame(data['ComplianceStatus'].value_counts())


display(cible1,pg.welch_anova(data=data,dv=cible1,between='ComplianceStatus'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='ComplianceStatus'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


data.drop('ComplianceStatus',axis=1,inplace=True)


pd.DataFrame(data['Outlier'].value_counts())


pd.DataFrame(data['DataYear'].value_counts())


data.drop('DataYear',axis=1,inplace=True)


data['CouncilDistrictCode'].value_counts()

7    533
2    398
3    202
4    170
5    131
1    117
6    113
Name: CouncilDistrictCode, dtype: int64


data['CouncilDistrictCode']=data['CouncilDistrictCode'].apply(str)


data['CouncilDistrictCode'].value_counts()

7    533
2    398
3    202
4    170
5    131
1    117
6    113
Name: CouncilDistrictCode, dtype: int64


display(cible1,pg.welch_anova(data=data,dv=cible1,between='CouncilDistrictCode'))
display(cible2,pg.welch_anova(data=data,dv=cible2,between='CouncilDistrictCode'))

'SiteEnergyUse(kBtu)'

'TotalGHGEmissions'


def rapport_cible(table,var,cible):
    # Affichage du scatterplot cible vs var avec droite de régression
    sns.residplot(data=table, x=var, y=cible,  lowess=True, order=1, robust=True, line_kws={'color': 'red'})
    plt.suptitle('Relation linéaire entre '+var+' et '+cible,y=1.05,fontsize=(14))
    plt.title('R²= '+str(table[var].corr(table[cible],method='pearson').round(4)))


# Création d'une liste contenant les noms des variables quantitatives
Lst_quant=pd.DataFrame({'Variables quantitatives':data.iloc[:,2:].select_dtypes('number').columns})
Lst_quant


var1='YearBuilt'
Distribution(data[var1],'Distribution de '+var1,'année',unite=1)

<class 'int'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)


data[var1]=data[var1].apply(lambda x : 2016-x)


Distribution(data[var1],'Distribution de '+var1,'année',unite=1)

<class 'int'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)


rapport_cible(data,var1,cible1)


rapport_cible(data,var1,cible2)


var2='NumberofBuildings'
Distribution(data[var2],'Distribution de '+var2,'nombre de batiments',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


# Liste des index des propriétés concernées
id_multi_bat=data[data[var2]>1].index
data[data[var2]>1].shape

(53, 26)


VARS=['PropertyGFATotal','PropertyGFAParking','PropertyGFABuilding(s)',
     'LargestPropertyUseTypeGFA','SecondLargestPropertyUseTypeGFA','ThirdLargestPropertyUseTypeGFA',cible1,cible2]
for col in VARS:
    data.loc[id_multi_bat,col]=data.loc[id_multi_bat,col]/data.loc[id_multi_bat,var2]


data.drop(var2,axis=1,inplace=True)


var4='NumberofFloors'
Distribution(data[var4],'Distribution de '+var4)
Distribution(data[var4],'Distribution de '+var4+' sans outliers',fliers=False)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)
<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>
<class 'int'>


rapport_cible(data,var4,cible1)


rapport_cible(data,var4,cible2)


var5='PropertyGFATotal'
Distribution(data[var5],'Distribution de '+var5,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var5].apply(np.log1p),'Distribution de log( '+var5+' +1)',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)
<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>
<class 'int'>


data[str('log_'+var5)]=data[var5].apply(np.log1p)


rapport_cible(data,var5,cible1)


rapport_cible(data,var5,cible2)


var6='PropertyGFAParking'
Distribution(data[var6],'Distribution de '+var6,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var6].apply(np.log1p),'Distribution de log( '+var6+' +1)',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)
<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>
<class 'int'>


data[str('log_'+var6)]=data[var6].apply(np.log1p)


rapport_cible(data,var6,cible1)

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\statsmodels\nonparametric\smoothers_lowess.py:214: RuntimeWarning: invalid value encountered in true_divide
  res, _ = _lowess(y, x, x, np.ones_like(x),


rapport_cible(data,var6,cible2)

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\statsmodels\nonparametric\smoothers_lowess.py:214: RuntimeWarning: invalid value encountered in true_divide
  res, _ = _lowess(y, x, x, np.ones_like(x),


var7='PropertyGFABuilding(s)'
Distribution(data[var7],'Distribution de '+var7,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var7].apply(np.log1p),'Distribution de log( '+var7+' +1 )')

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data[str('log_'+var7)]=data[var7].apply(np.log1p)


rapport_cible(data,var7,cible1)


rapport_cible(data,var7,cible2)


var8='LargestPropertyUseTypeGFA'
Distribution(data[var8],'Distribution de '+var8,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var8].apply(np.log1p),'Distribution de log( '+var8+' +1 )')

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)
<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>
<class 'int'>


data[str('log_'+var8)]=data[var8].apply(np.log)


rapport_cible(data,var8,cible1)


rapport_cible(data,var8,cible2)


var9='SecondLargestPropertyUseTypeGFA'
Distribution(data[var9],'Distribution de '+var9,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var9].apply(np.log1p),'Distribution de log( '+var9+' +1 )')

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data[str('log_'+var9)]=data[var9].apply(np.log1p)


rapport_cible(data,var9,cible1)


rapport_cible(data,var9,cible2)


var10='ThirdLargestPropertyUseTypeGFA'
Distribution(data[var10],'Distribution de '+var10,'(en milliers de pieds²)',unite=1E3)
Distribution(data[var10].apply(np.log1p),'Distribution de log( '+var10+' +1 )')

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'float'>
<class 'int'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)


data[str('log_'+var10)]=data[var10].apply(np.log1p)


rapport_cible(data,var10,cible1)

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\statsmodels\nonparametric\smoothers_lowess.py:214: RuntimeWarning: invalid value encountered in true_divide
  res, _ = _lowess(y, x, x, np.ones_like(x),


rapport_cible(data,var10,cible2)

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\statsmodels\nonparametric\smoothers_lowess.py:214: RuntimeWarning: invalid value encountered in true_divide
  res, _ = _lowess(y, x, x, np.ones_like(x),


# Création de nouvelles variables en divisant les surfaces par le nombre d'étages
data['LargestPropertyUseTypeGFA_per_Floors']=data['LargestPropertyUseTypeGFA']/data['NumberofFloors']
# data['LargestPropertyUseTypeGFA:NumberofBuildings']=data['LargestPropertyUseTypeGFA']/data['NumberofBuildings']

data['PropertyGFABuilding(s)_per_Floors']=data['PropertyGFABuilding(s)']/data['NumberofFloors']
# data['PropertyGFABuilding(s):NumberofBuildings']=data['PropertyGFABuilding(s)']/data['NumberofBuildings']

data['PropertyGFATotal_per_Floors']=data['PropertyGFATotal']/data['NumberofFloors']
# data['PropertyGFATotal:NumberofBuildings']=data['PropertyGFATotal']/data['NumberofBuildings']

data.drop('NumberofFloors',axis=1,inplace=True)
data.shape

(1664, 33)


Distribution(data['PropertyGFATotal_per_Floors'],unite=1)
Distribution(data['PropertyGFATotal_per_Floors'].apply(np.log1p),unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data['PropertyGFATotal_per_Floors']=data['PropertyGFATotal_per_Floors'].apply(np.log)


Distribution(data['PropertyGFABuilding(s)_per_Floors'],unite=1)
Distribution(data['PropertyGFABuilding(s)_per_Floors'].apply(np.log1p),unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data['PropertyGFABuilding(s)_per_Floors']=data['PropertyGFABuilding(s)_per_Floors'].apply(np.log)


Distribution(data['PropertyGFATotal_per_Floors'],unite=1)
Distribution(data['PropertyGFATotal_per_Floors'].apply(np.log1p),unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)
<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>
<class 'int'>


data['PropertyGFATotal_per_Floors']=data['PropertyGFATotal_per_Floors'].apply(np.log)


usages=['LargestPropertyUseType', 'SecondLargestPropertyUseType',
        'ThirdLargestPropertyUseType',
        'Use4', 'Use5', 'Use6', 'Use7', 'Use8']


data_use=data[usages]
data_use


data['Nbre_Use']=8-data_use[data_use!= 'None'].isnull().sum(axis=1)
data['Nbre_Use']

272     1
312     4
358     2
405     2
498     2
       ..
6711    1
6712    1
6713    3
6714    4
6715    4
Name: Nbre_Use, Length: 1664, dtype: int64


# Changement du nom de la variable
data.rename(columns={'SiteEnergyUse(kBtu)': 'SiteEnergyUse'},inplace=True)


Distribution(data['SiteEnergyUse'],'Distribution de SiteEnergyUse','kBtu',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


# Expression en million de kBtu                 
data['SiteEnergyUse'] = data['SiteEnergyUse'].apply(lambda x: np.log(x * 1E-6))


Distribution(data['SiteEnergyUse'],'Après transformation au log ',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


Distribution(data['TotalGHGEmissions'],'Distribution de TotalGHGEmissions','tonnes de CO2', unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data['TotalGHGEmissions'] = data['TotalGHGEmissions'].apply(np.log)
Distribution(data['TotalGHGEmissions'],'Après transformation au log',unite=1)

<ipython-input-56-fa81e029ec3d>:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_xticklabels(val/unite)

<class 'int'>


data.drop(['PropertyGFATotal',
           'PropertyGFAParking',
           'PropertyGFABuilding(s)', 
           'LargestPropertyUseTypeGFA', 
           'SecondLargestPropertyUseTypeGFA',
           'ThirdLargestPropertyUseTypeGFA'],axis=1,inplace=True)


data.describe()


msno.matrix(data,color=bleu)

<AxesSubplot:>


data.shape

(1664, 28)


def get_feature_correlation(df,target=None, top_n=None, corr_method='spearman',
                            remove_duplicates=True, remove_self_correlations=True):
    """
    Compute the feature correlation and sort feature pairs based on their correlation

    :param df: The dataframe with the predictor variables
    :type df: pandas.core.frame.DataFrame
    :param top_n: Top N feature pairs to be reported (if None, all of the pairs will be returned)
    :param corr_method: Correlation compuation method
    :type corr_method: str
    :param remove_duplicates: Indicates whether duplicate features must be removed
    :type remove_duplicates: bool
    :param remove_self_correlations: Indicates whether self correlations will be removed
    :type remove_self_correlations: bool

    :return: pandas.core.frame.DataFrame
    """
    corr_matrix_abs = df.corr(method=corr_method).abs()
#     display(corr_matrix_abs )
    corr_matrix_abs_us = corr_matrix_abs.unstack()
    sorted_correlated_features = corr_matrix_abs_us \
        .sort_values(kind="quicksort", ascending=False) \
        .reset_index()
        
    # just keep corrélation to target
    if target:
        corr_matrix_abs.drop(target,axis=0,inplace=True)
        to_target=corr_matrix_abs[target].sort_values(kind="quicksort", ascending=False).reset_index()
        to_target.columns = [target,  'Correlation (abs)'] 
        return to_target
    
    # Remove comparisons of the same feature
    if remove_self_correlations:
        sorted_correlated_features = sorted_correlated_features[
            (sorted_correlated_features.level_0 != sorted_correlated_features.level_1)
        ]

    # Remove duplicates
    if remove_duplicates:
        sorted_correlated_features = sorted_correlated_features.iloc[:-2:2]

    # Create meaningful names for the columns
    sorted_correlated_features.columns = ['Feature 1', 'Feature 2', 'Correlation (abs)'] 

    if top_n:
        return sorted_correlated_features[:top_n]

    return sorted_correlated_features


quant=data.select_dtypes('number')


quant=data.select_dtypes('number')
correlations_energy=get_feature_correlation(quant,target='SiteEnergyUse')
correlations_ges=get_feature_correlation(quant,target='TotalGHGEmissions')
display(correlations_energy)
correlations_ges


correlations_energy=correlations_energy[correlations_energy['Correlation (abs)']<.7]
correlations_ges=correlations_ges[correlations_ges['Correlation (abs)']<.7]
display(correlations_energy)
correlations_ges


correlations_energy['SiteEnergyUse'].sort_values().to_list()==correlations_ges['TotalGHGEmissions'].sort_values().to_list()

False


sel_feat=['SiteEnergyUse','TotalGHGEmissions']

sel_feat.extend(correlations_energy['SiteEnergyUse'].sort_values().to_list())


cat = data.select_dtypes('object').columns.to_list()
Energy_eta2 = pd.DataFrame(columns=['Source','ddof1','ddof2','F', 'p-unc', 'np2'])

for col in cat:
    etaE = pg.welch_anova(data=data, dv='SiteEnergyUse', between=col)
    Energy_eta2 = pd.concat([Energy_eta2, etaE],axis=0)

# Energy_eta2.columns = ['Energy /Variable', 'p value', 'np2']
display(Energy_eta2)
print('Elimination des p_values supérieures à 0.05')

usefull_cat_ENR = Energy_eta2[Energy_eta2['p-unc'] < .05].sort_values(
    by='np2', ascending=False)
usefull_cat_ENR

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\pingouin\parametric.py:1330: RuntimeWarning: divide by zero encountered in double_scalars
  pval = f.sf(fval, ddof1, 1 / lamb)
C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\pingouin\parametric.py:1336: RuntimeWarning: divide by zero encountered in double_scalars
  'ddof2': 1 / lamb,

Elimination des p_values supérieures à 0.05


cat = data.select_dtypes('object').columns.to_list()
Ges_eta2 = pd.DataFrame(columns=['Source','ddof1','ddof2','F', 'p-unc', 'np2'])

for col in cat:
    etaE = pg.welch_anova(data=data, dv='TotalGHGEmissions', between=col)
    Ges_eta2 = pd.concat([Ges_eta2, etaE],axis=0)

# Energy_eta2.columns = ['Energy /Variable', 'p value', 'np2']
display(Ges_eta2)
print('Elimination des p_values supérieures à 0.05')

usefull_cat_GES = Ges_eta2[Ges_eta2['p-unc'] < .05].sort_values(
    by='np2', ascending=False)
usefull_cat_GES

C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\pingouin\parametric.py:1330: RuntimeWarning: divide by zero encountered in double_scalars
  pval = f.sf(fval, ddof1, 1 / lamb)
C:\Users\demo\anaconda3\envs\Python 3 Tabnine\lib\site-packages\pingouin\parametric.py:1336: RuntimeWarning: divide by zero encountered in double_scalars
  'ddof2': 1 / lamb,

Elimination des p_values supérieures à 0.05


sel_feat.extend(usefull_cat_GES['Source'])


data[sel_feat].head(0).T


data[sel_feat].shape

(1664, 22)


data[sel_feat].describe()


data[sel_feat].to_json('Buildings_STAR_modelisation.json')

	OSEBuildingID	DataYear	BuildingType	PrimaryPropertyType	PropertyName	TaxParcelIdentificationNumber	Location	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFAParking	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEUI(kBtu/sf)	SiteEUIWN(kBtu/sf)	SourceEUI(kBtu/sf)	SourceEUIWN(kBtu/sf)	SiteEnergyUse(kBtu)	SiteEnergyUseWN(kBtu)	SteamUse(kBtu)	Electricity(kWh)	Electricity(kBtu)	NaturalGas(therms)	NaturalGas(kBtu)	OtherFuelUse(kBtu)	GHGEmissions(MetricTonsCO2e)	GHGEmissionsIntensity(kgCO2e/ft2)	DefaultData	Comment	ComplianceStatus	Outlier	2010 Census Tracts	Seattle Police Department Micro Community Policing Plan Areas	City Council Districts	SPD Beats	Zip Codes
0	1	2015	NonResidential	Hotel	MAYFLOWER PARK HOTEL	659000030	{'latitude': '47.61219025', 'longitude': '-122...	7	DOWNTOWN	1927	1	12.0	88434	0	88434	Hotel	Hotel	88434.0	NaN	NaN	NaN	NaN	NaN	65.0	78.9	80.3	173.5	175.1	6981428.0	7097539.0	2023032.0	1080307.0	3686160.0	12724.0	1272388.0	0.0	249.43	2.64	No	NaN	Compliant	NaN	NaN	14.0	NaN	31.0	18081
1	2	2015	NonResidential	Hotel	PARAMOUNT HOTEL	659000220	{'latitude': '47.61310583', 'longitude': '-122...	7	DOWNTOWN	1996	1	11.0	103566	15064	88502	Hotel, Parking, Restaurant	Hotel	83880.0	Parking	15064.0	Restaurant	4622.0	NaN	51.0	94.4	99.0	191.3	195.2	8354235.0	8765788.0	0.0	1144563.0	3905411.0	44490.0	4448985.0	0.0	263.51	2.38	No	NaN	Compliant	NaN	NaN	14.0	NaN	31.0	18081

	0
id	h7rm-fz6m
name	2015 Building Energy Benchmarking
assetType	dataset
attribution	City of Seattle
attributionLink	http://www.seattle.gov/environment
averageRating	0
createdAt	1492461362
description	Seattle’s Building Energy Benchmarking and Rep...
displayType	table
downloadCount	2546
hideFromCatalog	False
hideFromDataJson	False
indexUpdatedAt	1551379229
licenseId	PUBLIC_DOMAIN
newBackend	True
numberOfComments	0
oid	28249767
provenance	official
publicationAppendEnabled	False
publicationDate	1521489624
publicationGroup	14152737
publicationStage	published
rowsUpdatedAt	1521489255
rowsUpdatedBy	9yyf-ecsx
tableId	14945997
totalTimesRated	0
viewCount	4217
viewLastModified	1557955129
viewType	tabular
approvals	[{'reviewedAt': 1521489624, 'reviewedAutomatic...
columns	[{'id': 350402298, 'name': 'OSEBuildingID', 'd...
grants	[{'inherited': False, 'type': 'viewer', 'flags...
license	{'name': 'Public Domain'}
metadata	{'rdfSubject': '0', 'jsonQuery': {'order': [{'...
owner	{'id': '9yyf-ecsx', 'displayName': 'Sullivan, ...
query	{'orderBys': [{'ascending': True, 'expression'...
rights	[read]
tableAuthor	{'id': '9yyf-ecsx', 'displayName': 'Sullivan, ...
tags	[building, eui, benchmarking, energy, electric...
flags	[default, restorable, restorePossibleForType]

	id	name	dataTypeName	description	fieldName	position	renderTypeName	tableColumnId	width	cachedContents	format	computationStrategy
0	350402298	OSEBuildingID	number	A unique identifier assigned to each property ...	seattlebuildingid	1	number	46302822	182.0	{'largest': '50059', 'non_null': 3340, 'averag...	{'precisionStyle': 'standard', 'noCommas': 'tr...	NaN
1	350402299	DataYear	text	NaN	datayear	2	text	46302823	196.0	{'largest': '2015', 'non_null': 3340, 'null': ...	{}	NaN
2	350402300	BuildingType	text	City of Seattle building type classification.	buildingtype	3	text	46302824	244.0	{'largest': 'SPS-District K-12', 'non_null': 3...	{'displayStyle': 'plain', 'align': 'left'}	NaN
3	350402301	PrimaryPropertyType	text	The primary use of a property (e.g. office, re...	primarypropertytype	4	text	46302825	328.0	{'largest': 'Worship Facility', 'non_null': 33...	{'displayStyle': 'plain', 'align': 'left'}	NaN
4	350402302	PropertyName	text	Official or common property name.	propertyname	5	text	46302826	244.0	{'largest': 'ZYMOGENETICS-DENDREON', 'non_null...	{'displayStyle': 'plain', 'align': 'left'}	NaN
5	350402873	TaxParcelIdentificationNumber	text	\t\nProperty King County PIN	taxparcelidentificationnumber	6	text	46302828	448.0	{'largest': '9835200050', 'non_null': 3338, 'n...	{'precisionStyle': 'standard', 'noCommas': 'tr...	NaN
6	350402305	Location	location	NaN	location	7	location	46302864	384.0	NaN	{'view': 'address', 'align': 'left'}	NaN
7	350402306	CouncilDistrictCode	number	Property City of Seattle council district.	councildistrictcode	12	number	46302829	328.0	{'largest': '7', 'non_null': 3340, 'average': ...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
8	350402307	Neighborhood	text	NaN	neighborhood	13	text	46302830	244.0	{'largest': 'SOUTHWEST', 'non_null': 3340, 'nu...	{}	NaN
9	350402308	YearBuilt	number	Year in which a property was constructed or un...	yearbuilt	14	number	46302831	208.0	{'largest': '2015', 'non_null': 3340, 'average...	{'precisionStyle': 'standard', 'noCommas': 'tr...	NaN
10	350402309	NumberofBuildings	number	NaN	numberofbuildings	15	number	46302832	304.0	{'largest': '39', 'non_null': 3340, 'average':...	{}	NaN
11	350402310	NumberofFloors	number	NaN	numberoffloors	16	number	46302833	268.0	{'largest': '99', 'non_null': 3332, 'average':...	{}	NaN
12	350402311	PropertyGFATotal	number	Total building and parking gross floor area.	propertygfatotal	17	number	46302834	292.0	{'largest': '2200000', 'non_null': 3340, 'aver...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
13	350402312	PropertyGFAParking	number	Total space in square feet of all types of par...	propertygfaparking	18	number	46302835	316.0	{'largest': '512608', 'non_null': 3340, 'avera...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
14	350402313	PropertyGFABuilding(s)	number	Total floor space in square feet between the o...	propertygfabuilding_s	19	number	46302836	364.0	{'largest': '2200000', 'non_null': 3340, 'aver...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
15	350402314	ListOfAllPropertyUseTypes	text	NaN	listofallpropertyusetypes	20	text	46302837	400.0	{'largest': 'Worship Facility', 'non_null': 32...	{}	NaN
16	350402315	LargestPropertyUseType	text	NaN	largestpropertyusetype	21	text	46302838	364.0	{'largest': 'Worship Facility', 'non_null': 32...	{}	NaN
17	350402316	LargestPropertyUseTypeGFA	number	NaN	largestpropertyusetypegfa	22	number	46302839	400.0	{'largest': '1719643', 'non_null': 3204, 'aver...	{}	NaN
18	350402317	SecondLargestPropertyUseType	text	NaN	secondlargestpropertyusetype	23	text	46302840	436.0	{'largest': 'Worship Facility', 'non_null': 15...	{}	NaN
19	350402318	SecondLargestPropertyUseTypeGFA	number	NaN	secondlargestpropertyusetypegfa	24	number	46302841	472.0	{'largest': '686750', 'non_null': 1559, 'avera...	{}	NaN
20	350402319	ThirdLargestPropertyUseType	text	NaN	thirdlargestpropertyusetype	25	text	46302842	424.0	{'largest': 'Worship Facility', 'non_null': 56...	{}	NaN
21	350402320	ThirdLargestPropertyUseTypeGFA	number	NaN	thirdlargestpropertyusetypegfa	26	number	46302843	460.0	{'largest': '303910', 'non_null': 560, 'averag...	{}	NaN
22	350402321	YearsENERGYSTARCertified	text	NaN	yearsenergystarcertified	27	text	46302844	388.0	{'largest': '2017, 2016, 2014, 2013, 2012, 201...	{}	NaN
23	350402322	ENERGYSTARScore	number	An EPA calculated 1-100 rating that assesses a...	energystarscore	28	number	46302845	280.0	{'largest': '100.00', 'non_null': 2560, 'avera...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
24	350402323	SiteEUI(kBtu/sf)	number	Site Energy Use Intensity (EUI) is a property'...	siteeui_kbtu_sf	29	number	46302846	292.0	{'largest': '800.60', 'non_null': 3330, 'avera...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
25	350402324	SiteEUIWN(kBtu/sf)	number	\t\nWeather Normalized (WN) Site Energy Use In...	siteeuiwn_kbtu_sf	30	number	46302847	316.0	{'largest': '800.60', 'non_null': 3330, 'avera...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
26	350402325	SourceEUI(kBtu/sf)	number	Source Energy Use Intensity (EUI) is a propert...	sourceeui_kbtu_sf	31	number	46302848	316.0	{'largest': '2511.10', 'non_null': 3330, 'aver...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
27	350402326	SourceEUIWN(kBtu/sf)	number	Weather Normalized (WN) Source Energy Use Inte...	sourceeuiwn_kbtu_sf	32	number	46302849	340.0	{'largest': '2511.00', 'non_null': 3330, 'aver...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
28	350402327	SiteEnergyUse(kBtu)	number	\t\nThe annual amount of energy consumed by th...	siteenergyuse_kbtu	33	number	46302850	328.0	{'largest': '295812640', 'non_null': 3330, 'av...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
29	350402328	SiteEnergyUseWN(kBtu)	number	NaN	siteenergyusewn_kbtu	34	number	46302851	352.0	{'largest': '297740960', 'non_null': 3330, 'av...	{}	NaN
30	350402329	SteamUse(kBtu)	number	The annual amount of district steam consumed b...	steamuse_kbtu	35	number	46302852	268.0	{'largest': '127869744', 'non_null': 3330, 'av...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
31	350402330	Electricity(kWh)	number	\t\nThe annual amount of electricity consumed ...	electricity_kwh	36	number	46302853	292.0	{'largest': '83445048', 'non_null': 3330, 'ave...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
32	350402331	Electricity(kBtu)	number	\t\nThe annual amount of electricity consumed ...	electricity_kbtu	37	number	46302854	304.0	{'largest': '284726322', 'non_null': 3330, 'av...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
33	350402332	NaturalGas(therms)	number	The annual amount of utility-supplied natural ...	naturalgas_therms	38	number	46302855	316.0	{'largest': '1364484', 'non_null': 3330, 'aver...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
34	350402333	NaturalGas(kBtu)	number	NaN	naturalgas_kbtu	39	number	46302856	292.0	{'largest': '136448438', 'non_null': 3330, 'av...	{}	NaN
35	350402334	OtherFuelUse(kBtu)	number	NaN	otherfueluse_kbtu	40	number	46302857	316.0	{'largest': '8269669', 'non_null': 3330, 'aver...	{}	NaN
36	350402335	GHGEmissions(MetricTonsCO2e)	number	The total amount of greenhouse gas emissions, ...	ghgemissions_metrictonsco2e	41	number	46302858	436.0	{'largest': '11824.89', 'non_null': 3330, 'ave...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
37	350402336	GHGEmissionsIntensity(kgCO2e/ft2)	number	\t\nTotal Greenhouse Gas Emissions divided by ...	ghgemissionsintensity_kgco2e_ft2	42	number	46302859	496.0	{'largest': '31.38', 'non_null': 3330, 'averag...	{'precisionStyle': 'standard', 'noCommas': 'fa...	NaN
38	350402337	DefaultData	text	NaN	defaultdata	43	text	46302860	232.0	{'largest': 'Yes', 'non_null': 3339, 'null': '...	{}	NaN
39	350402338	Comment	text	NaN	comment	44	text	46302861	184.0	{'largest': 'Under construction starting 6/201...	{}	NaN
40	350402339	ComplianceStatus	text	NaN	compliancestatus	45	text	46302862	292.0	{'largest': 'Not Compliant', 'non_null': 3340,...	{}	NaN
41	350402340	Outlier	text	NaN	outlier	46	text	46302863	184.0	{'largest': 'Low Outlier', 'non_null': 84, 'nu...	{}	NaN
42	350404102	2010 Census Tracts	number	NaN	:@computed_region_2day_rhn5	47	number	62603791	NaN	NaN	{}	{'source_columns': ['location'], 'type': 'geor...
43	350404103	Seattle Police Department Micro Community Poli...	number	NaN	:@computed_region_ru88_fbhk	48	number	62603792	NaN	NaN	{}	{'source_columns': ['location'], 'type': 'geor...
44	350404104	City Council Districts	number	NaN	:@computed_region_cyqu_gs94	49	number	62603793	NaN	NaN	{}	{'source_columns': ['location'], 'type': 'geor...
45	350404105	SPD Beats	number	NaN	:@computed_region_kuhn_3gp2	50	number	62603794	NaN	NaN	{}	{'source_columns': ['location'], 'type': 'geor...
46	350404106	Zip Codes	number	NaN	:@computed_region_q256_3sug	51	number	62603795	NaN	NaN	{}	{'source_columns': ['location'], 'type': 'geor...

	0
id	2bpz-gwpy
name	2016 Building Energy Benchmarking
assetType	dataset
attribution	City of Seattle
attributionLink	https://www.seattle.gov/energybenchmarking
averageRating	0
createdAt	1521070051
description	Seattle’s Building Energy Benchmarking and Rep...
displayType	table
downloadCount	2024
hideFromCatalog	False
hideFromDataJson	False
indexUpdatedAt	1521490072
licenseId	PUBLIC_DOMAIN
newBackend	True
numberOfComments	0
oid	28218043
provenance	official
publicationAppendEnabled	False
publicationDate	1521071079
publicationGroup	14936543
publicationStage	published
rowIdentifierColumnId	349768636
rowsUpdatedAt	1521071078
rowsUpdatedBy	9yyf-ecsx
tableId	14936543
totalTimesRated	0
viewCount	2234
viewLastModified	1557954938
viewType	tabular
approvals	[{'reviewedAt': 1521071079, 'reviewedAutomatic...
columns	[{'id': 349768636, 'name': 'OSEBuildingID', 'd...
grants	[{'inherited': False, 'type': 'viewer', 'flags...
license	{'name': 'Public Domain'}
metadata	{'sidebar': {'width': 482}, 'custom_fields': {...
owner	{'id': '9yyf-ecsx', 'displayName': 'Sullivan, ...
query	{}
rights	[read]
tableAuthor	{'id': '9yyf-ecsx', 'displayName': 'Sullivan, ...
tags	[building, eui, energy, steam, electricity, gr...
flags	[default, restorable, restorePossibleForType]

	id	name	dataTypeName	description	fieldName	position	renderTypeName	tableColumnId	cachedContents	format	width
0	349768636	OSEBuildingID	number	A unique identifier assigned to each property ...	osebuildingid	1	number	62418341	{'largest': '50226', 'non_null': 3376, 'averag...	{'groupSeparator': ''}	NaN
1	349768619	DataYear	number	Calendar year (January-December) represented b...	datayear	2	number	62418324	{'largest': '2016', 'non_null': 3376, 'average...	{'precisionStyle': 'standard', 'noCommas': 'tr...	100.0
2	349768631	BuildingType	text	City of Seattle building type classification.	buildingtype	3	text	62418336	{'largest': 'SPS-District K-12', 'non_null': 3...	{}	NaN
3	349768622	PrimaryPropertyType	text	The primary use of a property (e.g. office, re...	primarypropertytype	4	text	62418327	{'largest': 'Worship Facility', 'non_null': 33...	{}	NaN
4	349768617	PropertyName	text	Official or common property name as entered in...	propertyname	5	text	62418322	{'largest': 'Zindorf Apartments', 'non_null': ...	{}	NaN
5	349768592	Address	text	Property street address	address	6	text	62418297	{'largest': 'Readiness Center - Pier 91', 'non...	{}	NaN
6	349768594	City	text	Property city	city	7	text	62418299	{'largest': 'Seattle', 'non_null': 3376, 'null...	{}	NaN
7	349768593	State	text	Property state	state	8	text	62418298	{'largest': 'WA', 'non_null': 3376, 'null': 0,...	{}	NaN
8	349768610	ZipCode	number	Property zip	zipcode	9	number	62418315	{'largest': '98272', 'non_null': 3360, 'averag...	{'precisionStyle': 'standard', 'noCommas': 'tr...	100.0
9	349768606	TaxParcelIdentificationNumber	text	Property King County PIN	taxparcelidentificationnumber	10	text	62418311	{'largest': '9835200050', 'non_null': 3376, 'n...	{}	NaN
10	349768629	CouncilDistrictCode	number	Property City of Seattle council district.	councildistrictcode	11	number	62418334	{'largest': '7', 'non_null': 3376, 'average': ...	{}	NaN
11	349768627	Neighborhood	text	Property neighborhood area defined by the City...	neighborhood	12	text	62418332	{'largest': 'SOUTHWEST', 'non_null': 3376, 'nu...	{}	NaN
12	349768591	Latitude	number	Property latitude.	latitude	13	number	62418296	{'largest': '47.73387', 'non_null': 3376, 'ave...	{}	NaN
13	349768598	Longitude	number	Property longitude.	longitude	14	number	62418303	{'largest': '-122.2209659', 'non_null': 3376, ...	{}	NaN
14	349768623	YearBuilt	number	Year in which a property was constructed or un...	yearbuilt	15	number	62418328	{'largest': '2015', 'non_null': 3376, 'average...	{'precisionStyle': 'standard', 'noCommas': 'tr...	100.0
15	349768607	NumberofBuildings	number	Number of buildings included in the property's...	numberofbuildings	16	number	62418312	{'largest': '111', 'non_null': 3368, 'average'...	{}	NaN
16	349768605	NumberofFloors	number	Number of floors reported in Portfolio Manager	numberoffloors	17	number	62418310	{'largest': '99', 'non_null': 3376, 'average':...	{}	NaN
17	349768602	PropertyGFATotal	number	Total building and parking gross floor area.	propertygfatotal	18	number	62418307	{'largest': '9320156', 'non_null': 3376, 'aver...	{}	NaN
18	349768616	PropertyGFAParking	number	Total space in square feet of all types of par...	propertygfaparking	19	number	62418321	{'largest': '512608', 'non_null': 3376, 'avera...	{}	NaN
19	349768635	PropertyGFABuilding(s)	number	Total floor space in square feet between the o...	propertygfabuilding_s	20	number	62418340	{'largest': '9320156', 'non_null': 3376, 'aver...	{}	NaN
20	349768595	ListOfAllPropertyUseTypes	text	All property uses reported in Portfolio Manager	listofallpropertyusetypes	21	text	62418300	{'largest': 'Worship Facility', 'non_null': 33...	{}	NaN
21	349768634	LargestPropertyUseType	text	The largest use of a property (e.g. office, re...	largestpropertyusetype	22	text	62418339	{'largest': 'Worship Facility', 'non_null': 33...	{}	NaN
22	349768612	LargestPropertyUseTypeGFA	number	The gross floor area (GFA) of the largest use ...	largestpropertyusetypegfa	23	number	62418317	{'largest': '9320156', 'non_null': 3356, 'aver...	{}	NaN
23	349768630	SecondLargestPropertyUseType	text	The second largest use of a property (e.g. off...	secondlargestpropertyusetype	24	text	62418335	{'largest': 'Worship Facility', 'non_null': 16...	{}	NaN
24	349768614	SecondLargestPropertyUseTypeGFA	number	The gross floor area (GFA) of the second large...	secondlargestpropertyuse	25	number	62418319	{'largest': '686750', 'non_null': 1679, 'avera...	{}	NaN
25	349768620	ThirdLargestPropertyUseType	text	The third largest use of a property (e.g. offi...	thirdlargestpropertyusetype	26	text	62418325	{'largest': 'Worship Facility', 'non_null': 60...	{}	NaN
26	349768625	ThirdLargestPropertyUseTypeGFA	number	The gross floor area (GFA) of the third larges...	thirdlargestpropertyusetypegfa	27	number	62418330	{'largest': '459748', 'non_null': 596, 'averag...	{}	NaN
27	349768600	YearsENERGYSTARCertified	number	Years the property has received ENERGY STAR ce...	yearsenergystarcertified	28	number	62418305	{'largest': '201620152014201320122011201020092...	{}	NaN
28	349768596	ENERGYSTARScore	text	An EPA calculated 1-100 rating that assesses a...	energystarscore	29	text	62418301	{'largest': 'NULL', 'non_null': 2541, 'null': ...	{}	NaN
29	349768609	SiteEUI(kBtu/sf)	number	Site Energy Use Intensity (EUI) is a property'...	siteeui_kbtu_sf	30	number	62418314	{'largest': '834.4000244', 'non_null': 3369, '...	{}	NaN
30	349768611	SiteEUIWN(kBtu/sf)	number	Weather Normalized (WN) Site Energy Use Intens...	siteeuiwn_kbtu_sf	31	number	62418316	{'largest': '834.4000244', 'non_null': 3370, '...	{}	NaN
31	349768633	SourceEUI(kBtu/sf)	number	Source Energy Use Intensity (EUI) is a propert...	sourceeui_kbtu_sf	32	number	62418338	{'largest': '2620', 'non_null': 3367, 'average...	{}	NaN
32	349768599	SourceEUIWN(kBtu/sf)	number	Weather Normalized (WN) Source Energy Use Inte...	sourceeuiwn_kbtu_sf	33	number	62418304	{'largest': '2620', 'non_null': 3367, 'average...	{}	NaN
33	349768628	SiteEnergyUse(kBtu)	number	The annual amount of energy consumed by the pr...	siteenergyuse_kbtu	34	number	62418333	{'largest': '873923712', 'non_null': 3371, 'av...	{}	NaN
34	349768603	SiteEnergyUseWN(kBtu)	number	The annual amount of energy consumed by the pr...	siteenergyusewn_kbtu	35	number	62418308	{'largest': '471613856', 'non_null': 3370, 'av...	{}	NaN
35	349768626	SteamUse(kBtu)	number	The annual amount of district steam consumed b...	steamuse_kbtu	36	number	62418331	{'largest': '134943456', 'non_null': 3367, 'av...	{}	NaN
36	349768613	Electricity(kWh)	number	The annual amount of electricity consumed by t...	electricity_kwh	37	number	62418318	{'largest': '192577488', 'non_null': 3367, 'av...	{}	NaN
37	349768624	Electricity(kBtu)	number	The annual amount of electricity consumed by t...	electricity_kbtu	38	number	62418329	{'largest': '657074389', 'non_null': 3367, 'av...	{}	NaN
38	349768621	NaturalGas(therms)	number	The annual amount of utility-supplied natural ...	naturalgas_therms	39	number	62418326	{'largest': '2979090', 'non_null': 3367, 'aver...	{}	NaN
39	349768608	NaturalGas(kBtu)	number	The annual amount of utility-supplied natural ...	naturalgas_kbtu	40	number	62418313	{'largest': '297909000', 'non_null': 3367, 'av...	{}	NaN
40	349768615	DefaultData	checkbox	The property used default data for at least on...	defaultdata	41	checkbox	62418320	{'largest': True, 'non_null': 3376, 'null': 0,...	{}	NaN
41	349768597	Comments	text	Comments by a building owner or agent to provi...	comments	42	text	62418302	{'non_null': 0, 'null': 3376}	{}	NaN
42	349768618	ComplianceStatus	text	Whether a property has met energy benchmarking...	compliancestatus	43	text	62418323	{'largest': 'Non-Compliant', 'non_null': 3376,...	{}	NaN
43	349768601	Outlier	text	Whether a property is a high or low outlier (Y/N)	outlier	44	text	62418306	{'largest': 'Low outlier', 'non_null': 32, 'nu...	{}	NaN
44	349768604	TotalGHGEmissions	text	The total amount of greenhouse gas emissions, ...	totalghgemissions	45	text	62418309	{'largest': '99.65', 'non_null': 3367, 'null':...	{'displayStyle': 'plain', 'align': 'left'}	100.0
45	349768632	GHGEmissionsIntensity	text	Total Greenhouse Gas Emissions divided by prop...	ghgemissionsintensity	46	text	62418337	{'largest': '9.98', 'non_null': 3367, 'null': ...	{}	NaN

Exploratory Data Analysis

Chargement des données¶

Variables cibles:¶

Suppression des variables concernant les relevés dont on veut se séparer¶

Recherche des erreurs de remplissage¶

Etude des valeurs manquantes¶

Distributions¶

Etude des variables qualitatives¶

Etude des varibles quantitatives¶

Variable : ThirdLargestPropertyUseTypeGFA¶

Feature engeneering¶

Sélection des variables¶

Choix des variables quantitatives¶

Calcul des corrélations des variables quantitatives avec la cible¶

Choix des variables catégorielles¶

Enregistrement des données pour la modélisation¶

	OSEBuildingID	DataYear	BuildingType	PrimaryPropertyType	PropertyName	Address	City	State	ZipCode	TaxParcelIdentificationNumber	CouncilDistrictCode	Neighborhood	Latitude	Longitude	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFAParking	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEUI(kBtu/sf)	SiteEUIWN(kBtu/sf)	SourceEUI(kBtu/sf)	SourceEUIWN(kBtu/sf)	SiteEnergyUse(kBtu)	SiteEnergyUseWN(kBtu)	SteamUse(kBtu)	Electricity(kWh)	Electricity(kBtu)	NaturalGas(therms)	NaturalGas(kBtu)	DefaultData	Comments	ComplianceStatus	Outlier	TotalGHGEmissions	GHGEmissionsIntensity
0	1	2016	NonResidential	Hotel	Mayflower park hotel	405 Olive way	Seattle	WA	98101.0	0659000030	7	DOWNTOWN	47.61220	-122.33799	1927	1.0	12	88434	0	88434	Hotel	Hotel	88434.0	NaN	NaN	NaN	NaN	NaN	60.0	81.699997	84.300003	182.500000	189.000000	7226362.5	7456910.0	2003882.0	1.156514e+06	3946027.0	12764.52930	1276453.0	False	NaN	Compliant	NaN	249.98	2.83
1	2	2016	NonResidential	Hotel	Paramount Hotel	724 Pine street	Seattle	WA	98101.0	0659000220	7	DOWNTOWN	47.61317	-122.33393	1996	1.0	11	103566	15064	88502	Hotel, Parking, Restaurant	Hotel	83880.0	Parking	15064.0	Restaurant	4622.0	NaN	61.0	94.800003	97.900002	176.100006	179.399994	8387933.0	8664479.0	0.0	9.504252e+05	3242851.0	51450.81641	5145082.0	False	NaN	Compliant	NaN	295.86	2.86

	DataYear	BuildingType	PrimaryPropertyType	PropertyName	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFAParking	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEnergyUse(kBtu)	TotalGHGEmissions	DefaultData	ComplianceStatus	Outlier
count	3422.000000	3422	3422	3422	3422.00000	3422	3422.000000	3422.000000	3422.000000	3.422000e+03	3422.000000	3.422000e+03	3406	3395	3.395000e+03	1698	1698.000000	602	602.000000	120	2574.000000	3.422000e+03	3422.000000	3422	3422	35
unique	NaN	8	26	3405	NaN	19	NaN	NaN	NaN	NaN	NaN	NaN	472	57	NaN	50	NaN	44	NaN	65	NaN	NaN	NaN	4	4	3
top	NaN	NonResidential	Low-Rise Multifamily	NORTHGATE PLAZA	NaN	DOWNTOWN	NaN	NaN	NaN	NaN	NaN	NaN	Multifamily Housing	Multifamily Housing	NaN	Parking	NaN	Retail Store	NaN	2016	NaN	NaN	NaN	False	Compliant	Low outlier
freq	NaN	1486	1002	3	NaN	585	NaN	NaN	NaN	NaN	NaN	NaN	874	1683	NaN	988	NaN	112	NaN	14	NaN	NaN	NaN	3254	3262	23
mean	2015.983928	NaN	NaN	NaN	4.44886	NaN	1968.364991	1.106955	4.692285	9.474116e+04	8185.718878	8.655545e+04	NaN	NaN	7.923501e+04	NaN	28482.410658	NaN	11694.299666	NaN	68.004662	5.424472e+06	120.539719	NaN	NaN	NaN
std	0.125773	NaN	NaN	NaN	2.11967	NaN	33.199569	2.094163	5.465658	2.179351e+05	33075.053259	2.067303e+05	NaN	NaN	2.011440e+05	NaN	54431.218483	NaN	29195.448352	NaN	26.795406	2.162899e+07	541.496716	NaN	NaN	NaN
min	2015.000000	NaN	NaN	NaN	1.00000	NaN	1900.000000	0.000000	0.000000	1.128500e+04	0.000000	3.636000e+03	NaN	NaN	5.656000e+03	NaN	0.000000	NaN	0.000000	NaN	1.000000	0.000000e+00	-0.800000	NaN	NaN	NaN
25%	2016.000000	NaN	NaN	NaN	3.00000	NaN	1948.000000	1.000000	2.000000	2.843200e+04	0.000000	2.764050e+04	NaN	NaN	2.505400e+04	NaN	5000.000000	NaN	2241.750000	NaN	53.000000	9.242540e+05	9.422500	NaN	NaN	NaN
50%	2016.000000	NaN	NaN	NaN	4.00000	NaN	1974.000000	1.000000	4.000000	4.417500e+04	0.000000	4.319150e+04	NaN	NaN	3.987000e+04	NaN	10664.000000	NaN	5043.000000	NaN	75.000000	1.797181e+06	33.790000	NaN	NaN	NaN
75%	2016.000000	NaN	NaN	NaN	7.00000	NaN	1997.000000	1.000000	5.000000	9.085975e+04	0.000000	8.414475e+04	NaN	NaN	7.571750e+04	NaN	26835.000000	NaN	10226.250000	NaN	90.000000	4.219434e+06	93.975000	NaN	NaN	NaN
max	2016.000000	NaN	NaN	NaN	7.00000	NaN	2015.000000	111.000000	99.000000	9.320156e+06	512608.000000	9.320156e+06	NaN	NaN	9.320156e+06	NaN	686750.000000	NaN	459748.000000	NaN	100.000000	8.739237e+08	16870.980000	NaN	NaN	NaN

	DataYear	BuildingType	PrimaryPropertyType	PropertyName	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	PropertyGFATotal	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEnergyUse(kBtu)	TotalGHGEmissions	DefaultData	ComplianceStatus	Outlier
3506	2016	NonResidential	Hotel	GRAND HYATT SEATTLE	7	DOWNTOWN	1999	1.0	934292	934292	Hotel, Other, Parking, Restaurant, Retail Store	Hotel	495536.0	Parking	438756.0	Restaurant	49700.0	NaN	NaN	6.504728e+07	1638.46	False	Compliant	NaN
3827	2016	NonResidential	Medical Office	ARNOLD PAVILION	3	EAST	2004	1.0	225982	225982	Medical Office	Medical Office	200184.0	NaN	NaN	NaN	NaN	NaN	53.0	2.056062e+07	692.12	False	Compliant	NaN
3828	2016	NonResidential	Mixed Use Property	2200 WESTLAKE - SEDO	7	DOWNTOWN	2006	0.0	516407	516407	Financial Office, Hotel, Multifamily Housing, ...	Parking	385196.0	Multifamily Housing	340236.0	Hotel	67627.0	NaN	31.0	3.940332e+07	734.72	False	Compliant	NaN
3904	2016	NonResidential	Other	PACIFIC PLACE	7	DOWNTOWN	1999	1.0	947987	947987	Office, Other - Entertainment/Public Assembly,...	Other - Mall	561684.0	Other - Entertainment/Public Assembly	100000.0	Restaurant	80000.0	NaN	NaN	4.651096e+07	324.25	False	Compliant	NaN
5094	2016	NonResidential	Medical Office	HART FIRST HILL LLC	3	EAST	1945	1.0	274568	274568	Medical Office, Parking	Medical Office	228133.0	Parking	135954.0	NaN	NaN	NaN	52.0	2.531153e+07	1110.07	False	Compliant	NaN
5333	2016	Campus	Other	(ID#24086)CAMPUS1:KC METRO TRANSIT ATLANTIC CE...	2	GREATER DUWAMISH	1991	10.0	230971	230971	Other, Parking	Other	230970.0	Parking	0.0	NaN	NaN	NaN	NaN	2.102229e+07	405.05	False	Compliant	NaN
6470	2016	NonResidential	Warehouse	SANDPOINT #5	4	NORTHEAST	1940	1.0	384772	384772	Non-Refrigerated Warehouse, Office	Non-Refrigerated Warehouse	349953.0	Office	63872.0	NaN	NaN	NaN	69.0	1.520676e+07	391.70	False	Compliant	NaN
6471	2016	NonResidential	Medical Office	SANDPOINT #25	4	NORTHEAST	1960	1.0	30287	30287	Medical Office	Medical Office	28050.0	NaN	NaN	NaN	NaN	NaN	32.0	2.193115e+06	32.72	False	Compliant	NaN
6472	2016	NonResidential	Small- and Mid-Sized Office	SANDPOINT #29	4	NORTHEAST	1960	1.0	21931	21931	Office	Office	31845.0	Office	31845.0	NaN	NaN	NaN	32.0	3.947209e+06	145.11	False	Compliant	NaN
6508	2016	Nonresidential COS	Other	MAGNUSON	4	NORTHEAST	1929	8.0	502030	502030	Other - Recreation	Other - Recreation	564258.0	NaN	NaN	NaN	NaN	NaN	NaN	1.847034e+07	549.11	False	Compliant	NaN
6613	2016	NonResidential	Other	SMILOW RAINIER VISTA BOYS & GIRLS CLUB	2	SOUTHEAST	2009	1.0	40265	40265	Pre-school/Daycare	Pre-school/Daycare	40265.0	NaN	NaN	NaN	NaN	NaN	NaN	2.159170e+06	51.39	False	Compliant	NaN
6614	2016	Campus	University	UNIVERSITY OF WASHINGTON - SEATTLE CAMPUS	4	NORTHEAST	1900	111.0	9320156	9320156	College/University	College/University	9320156.0	NaN	NaN	NaN	NaN	NaN	NaN	8.739237e+08	11140.56	False	Compliant	NaN
6616	2016	NonResidential	Residence Hall	CEDAR HALL	4	NORTHEAST	2011	1.0	176225	176225	Parking, Residence Hall/Dormitory	Residence Hall/Dormitory	176225.0	Parking	24000.0	NaN	NaN	NaN	91.0	4.926695e+06	103.65	False	Compliant	NaN
6618	2016	NonResidential	Residence Hall	LANDER HALL	4	NORTHEAST	1953	1.0	166991	166991	Residence Hall/Dormitory	Residence Hall/Dormitory	166991.0	NaN	NaN	NaN	NaN	NaN	19.0	1.596586e+07	437.20	False	Compliant	NaN
6619	2016	NonResidential	Residence Hall	MERCER HALL	4	NORTHEAST	1970	1.0	89392	89392	Residence Hall/Dormitory	Residence Hall/Dormitory	89392.0	NaN	NaN	NaN	NaN	NaN	16.0	6.960904e+06	48.53	False	Compliant	NaN
6620	2016	NonResidential	Residence Hall	POPLAR HALL	4	NORTHEAST	2011	1.0	97040	97040	Residence Hall/Dormitory	Residence Hall/Dormitory	97040.0	NaN	NaN	NaN	NaN	NaN	69.0	3.894142e+06	62.41	False	Compliant	NaN

	Valeurs manquantes	% du Total des valeurs
Outlier	3366	98.97
YearsENERGYSTARCertified	3282	96.50
ThirdLargestPropertyUseType	2800	82.33
ThirdLargestPropertyUseTypeGFA	2800	82.33
SecondLargestPropertyUseType	1706	50.16
SecondLargestPropertyUseTypeGFA	1706	50.16
ENERGYSTARScore	831	24.43
LargestPropertyUseType	27	0.79
LargestPropertyUseTypeGFA	27	0.79
ListOfAllPropertyUseTypes	16	0.47

	DataYear	BuildingType	PrimaryPropertyType	PropertyName	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFAParking	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEnergyUse(kBtu)	TotalGHGEmissions	DefaultData	ComplianceStatus	Outlier
3566	2016	NonResidential	Large Office	SECOND AND SPRING BUILDING	7	DOWNTOWN	1958	1.0	6.0	172842	25920	146922	Data Center, Office, Parking	Office	99890.0	Data Center	39336.0	Parking	25000.0	NaN	NaN	41399504.0	362.66	False	Non-Compliant	high outlier
3857	2016	NonResidential	Large Office	401 ELLIOTT AVE WEST	7	MAGNOLIA / QUEEN ANNE	2000	1.0	4.0	129551	42500	87051	Data Center, Office, Parking	Office	82273.0	Parking	42500.0	Data Center	4778.0	NaN	1.0	27137190.0	189.18	False	Non-Compliant	high outlier
5047	2016	Multifamily LR (1-4)	Mid-Rise Multifamily	TAYLOR ANNE CONDOMINIUM	7	MAGNOLIA / QUEEN ANNE	1969	1.0	5.0	30569	0	30569	Multifamily Housing	Multifamily Housing	30921.0	NaN	NaN	NaN	NaN	NaN	1.0	6834431.0	54.25	False	Non-Compliant	high outlier
6103	2016	Multifamily LR (1-4)	Low-Rise Multifamily	ATLAS BUILDING	2	DOWNTOWN	1920	1.0	4.0	46560	0	46560	Multifamily Housing, Restaurant, Retail Store	Multifamily Housing	46560.0	Retail Store	3000.0	Restaurant	2000.0	NaN	NaN	6881405.5	284.42	False	Non-Compliant	high outlier
6203	2016	Multifamily LR (1-4)	Low-Rise Multifamily	ST. JOHNS APARTMENTS	3	EAST	1910	1.0	3.0	24682	0	24682	Multifamily Housing, Retail Store	Multifamily Housing	17822.0	Retail Store	6085.0	NaN	NaN	NaN	74.0	2681771.0	96.18	False	Non-Compliant	high outlier
6262	2016	Multifamily LR (1-4)	Low-Rise Multifamily	VALI HAI	7	MAGNOLIA / QUEEN ANNE	1959	2.0	4.0	43346	0	43346	Multifamily Housing, Parking, Swimming Pool	Multifamily Housing	43346.0	Parking	12600.0	Swimming Pool	0.0	NaN	5.0	4519115.0	148.02	False	Non-Compliant	high outlier
6422	2016	Multifamily LR (1-4)	Low-Rise Multifamily	BUILDING 4	5	NORTH	2008	1.0	4.0	46725	0	46725	Multifamily Housing	Multifamily Housing	102522.0	NaN	NaN	NaN	NaN	NaN	1.0	13459221.0	531.96	False	Non-Compliant	high outlier
6432	2016	Multifamily MR (5-9)	Mid-Rise Multifamily	COMMODORE DUCHESS APARTMENTS	4	NORTHEAST	1925	1.0	8.0	97923	0	97923	Multifamily Housing	Multifamily Housing	70143.0	Office	220.0	NaN	NaN	NaN	5.0	10343027.0	686.53	False	Non-Compliant	high outlier
6605	2016	Multifamily LR (1-4)	Low-Rise Multifamily	STEWART HOUSE	7	DOWNTOWN	1904	1.0	3.0	49000	0	49000	Food Sales, Multifamily Housing, Restaurant, R...	Multifamily Housing	21600.0	Food Sales	5663.0	Retail Store	4027.0	NaN	NaN	4602793.5	78.39	False	Non-Compliant	high outlier

	DataYear	BuildingType	PrimaryPropertyType	PropertyName	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFABuilding(s)	ListOfAllPropertyUseTypes	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ThirdLargestPropertyUseTypeGFA	YearsENERGYSTARCertified	ENERGYSTARScore	SiteEnergyUse(kBtu)	TotalGHGEmissions	DefaultData	ComplianceStatus	Outlier
3693	2016	NonResidential	Self-Storage Facility	MARKET ST CENTER	6	BALLARD	1946	2.0	2.0	111445	111445	Fitness Center/Health Club/Gym, Office, Other ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.697472e+06	163.83	False	Compliant	normal
4417	2016	Multifamily HR (10+)	High-Rise Multifamily	THE CONCORD	7	DOWNTOWN	1999	1.0	13.0	219874	219874	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	17.0	1.321755e+07	408.82	False	Compliant	normal
4487	2016	NonResidential	Hotel	PALLADIAN HOTEL	7	DOWNTOWN	1910	1.0	8.0	61721	61721	Hotel	NaN	NaN	NaN	NaN	NaN	NaN	NaN	93.0	2.897080e+06	36.92	False	Compliant	normal
4823	2016	Multifamily MR (5-9)	Mid-Rise Multifamily	500 ELLIOTT HOMES CONDOMINIUM	7	MAGNOLIA / QUEEN ANNE	2000	1.0	5.0	45715	45715	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	72.0	1.289661e+06	8.99	False	Compliant	normal
5096	2016	Multifamily HR (10+)	High-Rise Multifamily	KELLEHER HOUSE	3	EAST	1982	1.0	17.0	76855	76855	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10.0	2.718223e+06	18.95	False	Compliant	normal
5676	2016	Multifamily LR (1-4)	Low-Rise Multifamily	MARIA CHALET	5	NORTHWEST	1978	1.0	4.0	20844	20844	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.597181e+05	3.90	False	Compliant	normal
5754	2016	NonResidential	Small- and Mid-Sized Office	TALON NORTHLAKE LLC	4	LAKE UNION	2008	1.0	4.0	48350	48350	Office	NaN	NaN	NaN	NaN	NaN	NaN	NaN	45.0	3.168131e+06	22.09	False	Compliant	normal
5799	2016	NonResidential	Restaurant	BUSH GARDEN - RESTURANT & LOUNGE	2	DOWNTOWN	1913	1.0	3.0	28800	28800	Restaurant	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	8.999242e+05	29.21	False	Compliant	normal
5885	2016	Multifamily LR (1-4)	Low-Rise Multifamily	WESTWOOD PLAZA CONDOMINIUMS	1	DELRIDGE	1981	1.0	3.0	35122	35122	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	43.0	9.365844e+05	6.53	False	Compliant	normal
6226	2016	Multifamily LR (1-4)	Low-Rise Multifamily	RAVENNA WOODS	5	NORTHEAST	1981	1.0	3.0	42448	42448	Multifamily Housing	NaN	NaN	NaN	NaN	NaN	NaN	NaN	66.0	1.247918e+06	8.70	False	Compliant	normal
6380	2016	Multifamily LR (1-4)	Low-Rise Multifamily	PINEHURST LANE	5	NORTH	1997	1.0	4.0	23175	23175	Multifamily Housing, Office	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7.152526e+05	16.31	False	Compliant	normal

	ListOfAllPropertyUseTypes
Multifamily Housing	866
Multifamily Housing, Parking	467
Office	143
Office, Parking	123
K-12 School	122
...	...
Multifamily Housing, Other - Education, Other - Entertainment/Public Assembly, Other - Restaurant/Bar	1
Medical Office, Office, Other	1
K-12 School, Office, Retail Store	1
Office, Other, Other - Restaurant/Bar, Strip Mall	1
Office, Other, Other - Lodging/Residential, Restaurant, Social/Meeting Hall	1

	SiteEnergyUse(kBtu)	TotalGHGEmissions	DataYear	BuildingType	PrimaryPropertyType	PropertyName	CouncilDistrictCode	Neighborhood	YearBuilt	NumberofBuildings	NumberofFloors	PropertyGFATotal	PropertyGFAParking	PropertyGFABuilding(s)	LargestPropertyUseType	LargestPropertyUseTypeGFA	SecondLargestPropertyUseType	SecondLargestPropertyUseTypeGFA	ThirdLargestPropertyUseType	ENERGYSTARScore	DefaultData	ComplianceStatus	Outlier	Use4	Use5	Use6	Use7	Use8
5107	1.045838e+06	44.26	2016	NonResidential	Other	LAKE CITY	5	NORTH	1969	1.0	1.0	30160	0	30160	Other	30139.0	None	0.0	None	NaN	False	Compliant	normal	None	None	None	None	None
5045	1.750362e+06	12.20	2016	Multifamily LR (1-4)	Low-Rise Multifamily	TAVONA CONDO HOA	4	LAKE UNION	2005	1.0	4.0	66013	0	66013	Multifamily Housing	48550.0	Parking	12032.0	None	36.0	False	Compliant	normal	None	None	None	None	None
5366	8.063703e+05	5.62	2016	Multifamily LR (1-4)	Low-Rise Multifamily	SS055 - 9818 5TH AVE NE	5	NORTH	1990	1.0	4.0	27344	0	27344	Multifamily Housing	21616.0	Parking	5728.0	None	31.0	False	Compliant	normal	None	None	None	None	None
4161	3.603765e+06	25.12	2016	Multifamily MR (5-9)	Mid-Rise Multifamily	SIDNEY APARTMENTS	7	DOWNTOWN	2000	1.0	6.0	160141	40377	119764	Multifamily Housing	132400.0	None	0.0	None	74.0	False	Compliant	normal	None	None	None	None	None
6001	5.715208e+05	3.98	2016	Multifamily LR (1-4)	Low-Rise Multifamily	QUEEN ANNE 20	7	MAGNOLIA / QUEEN ANNE	1977	1.0	4.0	29088	0	29088	Multifamily Housing	24288.0	Parking	4860.0	None	92.0	False	Compliant	normal	None	None	None	None	None

	PropertyName
CENTENNIAL BUILDING	2
BAYVIEW BUILDING	2
DEPT OF SOCIAL & HEALTH SERVICES	2
AIRPORT WAY	2
SOUTH PARK	2
...	...
US BANK CENTRE	1
ENTIRE CAMPUS	1
VIRGINIA MASON MEDICAL CENTER - 2149	1
UNIVERSITY BOOK STORE	1
EMERSON ELEMENTARY	1

	SiteEnergyUse	TotalGHGEmissions	YearBuilt	ENERGYSTARScore	log_PropertyGFATotal	log_PropertyGFAParking	log_PropertyGFABuilding(s)	log_LargestPropertyUseTypeGFA	log_SecondLargestPropertyUseTypeGFA	log_ThirdLargestPropertyUseTypeGFA	LargestPropertyUseTypeGFA_per_Floors	PropertyGFABuilding(s)_per_Floors	PropertyGFATotal_per_Floors	Nbre_Use
count	1664.000000	1664.000000	1664.000000	1103.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000	1664.000000
mean	1.056109	3.906402	54.319712	65.541251	10.996597	2.135837	10.915144	10.806396	4.535550	1.737606	28988.665580	9.959861	2.303683	1.788462
std	1.292575	1.503565	32.864194	28.359404	0.946555	4.248553	0.897681	0.949069	4.912377	3.539107	45232.377354	0.791661	0.077696	0.945139
min	-4.085847	-2.120264	1.000000	1.000000	8.366603	0.000000	8.198914	7.956687	0.000000	0.000000	221.696970	5.401311	1.686642	1.000000
25%	0.204679	2.989463	27.000000	49.000000	10.260182	0.000000	10.225372	10.110502	0.000000	0.000000	10685.520833	9.398955	2.248089	1.000000
50%	0.909161	3.895385	51.000000	73.000000	10.764339	0.000000	10.722967	10.638388	0.000000	0.000000	18470.000000	9.928936	2.303631	2.000000
75%	1.909228	4.902976	86.000000	89.000000	11.517653	0.000000	11.411006	11.377641	9.448727	0.000000	30803.297619	10.386377	2.350730	2.000000
max	6.105653	9.733350	116.000000	100.000000	14.603968	13.147269	14.603968	14.357627	13.369117	13.038436	859821.500000	13.910821	2.632667	8.000000

	Neighborhood
Downtown	363
Greater duwamish	348
Magnolia / queen anne	151
Lake union	147
Northeast	127
East	124
Northwest	87
Ballard	69
North	65
Central	51
Southeast	46
Delridge	46
Southwest	40

	DefaultData
No	1573
Yes	91

	ComplianceStatus
Compliant	1563
Error - Correct Default Data	87
Non-Compliant	14

	SiteEnergyUse	Correlation (abs)
0	TotalGHGEmissions	0.866207
1	log_PropertyGFATotal	0.733724
2	log_PropertyGFABuilding(s)	0.715881
3	log_LargestPropertyUseTypeGFA	0.696671
4	log_PropertyGFAParking	0.365840
5	log_SecondLargestPropertyUseTypeGFA	0.350302
6	PropertyGFATotal_per_Floors	0.344050
7	LargestPropertyUseTypeGFA_per_Floors	0.275835
8	YearBuilt	0.268536
9	PropertyGFABuilding(s)_per_Floors	0.260242
10	Nbre_Use	0.247116
11	log_ThirdLargestPropertyUseTypeGFA	0.216734
12	ENERGYSTARScore	0.174871

	TotalGHGEmissions	Correlation (abs)
1	log_PropertyGFABuilding(s)	0.569197
2	log_PropertyGFATotal	0.568170
3	log_LargestPropertyUseTypeGFA	0.552047
4	PropertyGFATotal_per_Floors	0.310408
5	LargestPropertyUseTypeGFA_per_Floors	0.266363
6	PropertyGFABuilding(s)_per_Floors	0.263469
7	log_SecondLargestPropertyUseTypeGFA	0.233988
8	log_PropertyGFAParking	0.213037
9	Nbre_Use	0.163429
10	YearBuilt	0.162071
11	log_ThirdLargestPropertyUseTypeGFA	0.156840
12	ENERGYSTARScore	0.141398

Source	ddof1	ddof2	F	p-unc	np2
BuildingType	4	1.289454e+02	0.865318	4.867584e-01	0.003206
PrimaryPropertyType	19	1.883967e+02	82.129843	2.056229e-80	0.384248
PropertyName	1657	1.972258e+05	0.007898	1.000000e+00	0.998876
CouncilDistrictCode	6	5.006442e+02	22.235397	3.002656e-23	0.076394
Neighborhood	12	3.716870e+02	14.194463	2.318412e-24	0.100105
LargestPropertyUseType	55	8.472567e+01	16.003617	1.365710e-27	0.290406
SecondLargestPropertyUseType	47	5.543685e+01	3.838447	1.315425e-06	0.145475
ThirdLargestPropertyUseType	39	4.177240e+01	9.937238	1.026352e-11	0.095020
Outlier	2	2.604375e+00	91.175770	3.883079e-03	0.054218
Use4	16	1.737988e+01	2.529046	3.200699e-02	0.040232
Use5	10	1.353492e+01	3.103233	2.781388e-02	0.020415
Use6	6	inf	0.000000	1.000000e+00	0.009945
Use7	4	inf	0.000000	1.000000e+00	0.006719
Use8	3	inf	0.000000	1.000000e+00	0.006595

Source	ddof1	ddof2	F	p-unc	np2
PrimaryPropertyType	19	188.396685	82.129843	2.056229e-80	0.384248
LargestPropertyUseType	55	84.725670	16.003617	1.365710e-27	0.290406
SecondLargestPropertyUseType	47	55.436849	3.838447	1.315425e-06	0.145475
Neighborhood	12	371.686996	14.194463	2.318412e-24	0.100105
ThirdLargestPropertyUseType	39	41.772404	9.937238	1.026352e-11	0.095020
CouncilDistrictCode	6	500.644156	22.235397	3.002656e-23	0.076394
Outlier	2	2.604375	91.175770	3.883079e-03	0.054218
Use4	16	17.379876	2.529046	3.200699e-02	0.040232
Use5	10	13.534922	3.103233	2.781388e-02	0.020415

Source	ddof1	ddof2	F	p-unc	np2
BuildingType	4	1.281407e+02	4.741179	1.336992e-03	0.009880
PrimaryPropertyType	19	1.876113e+02	43.119707	4.617300e-58	0.294230
PropertyName	1657	2.156350e+05	0.003353	1.000000e+00	0.997644
CouncilDistrictCode	6	4.983386e+02	14.549101	2.492572e-15	0.051260
Neighborhood	12	3.710607e+02	8.280259	7.051061e-14	0.060308
LargestPropertyUseType	55	8.482623e+01	14.735150	2.566104e-26	0.244130
SecondLargestPropertyUseType	47	5.827722e+01	2.073622	4.229841e-03	0.073498
ThirdLargestPropertyUseType	39	4.180814e+01	4.200177	5.585080e-06	0.058123
Outlier	2	2.560636e+00	48.841302	9.137514e-03	0.053676
Use4	16	1.746497e+01	4.186107	2.502938e-03	0.027806
Use5	10	1.370961e+01	6.324368	1.157912e-03	0.016497
Use6	6	inf	0.000000	1.000000e+00	0.008526
Use7	4	inf	0.000000	1.000000e+00	0.005270
Use8	3	inf	0.000000	1.000000e+00	0.004969

Source	ddof1	ddof2	F	p-unc	np2
PrimaryPropertyType	19	187.611277	43.119707	4.617300e-58	0.294230
LargestPropertyUseType	55	84.826226	14.735150	2.566104e-26	0.244130
SecondLargestPropertyUseType	47	58.277215	2.073622	4.229841e-03	0.073498
Neighborhood	12	371.060718	8.280259	7.051061e-14	0.060308
ThirdLargestPropertyUseType	39	41.808143	4.200177	5.585080e-06	0.058123
Outlier	2	2.560636	48.841302	9.137514e-03	0.053676
CouncilDistrictCode	6	498.338574	14.549101	2.492572e-15	0.051260
Use4	16	17.464969	4.186107	2.502938e-03	0.027806
Use5	10	13.709606	6.324368	1.157912e-03	0.016497
BuildingType	4	128.140683	4.741179	1.336992e-03	0.009880